ee81a23d6b83ac15fbeb5b7a30934e0b-Supplemental-Conference.pdf

WepresentanewclassofGAMs thatusetensor rank decompositions of polynomials to learn powerful,inherently-interpretable models. Our approach, titled Scalable Polynomial Additive Models (SPAM) is effortlessly scalable and modelsall higher-order feature interactions without a combinatorial parameter explosion. SPAM outperforms allcurrent interpretable approaches, and matches DNN/XGBoost performance onaseries ofreal-world benchmarks with up to hundreds of thousands of features.